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This paper presents a cooperative positioning method for multiple unmanned aerial vehicles (UAVs) based on yaw angle correction. As UAVs play an increasingly critical role in the expanding low-altitude airspace economy, achieving accurate and cost-effective localization remains a significant challenge. The proposed approach enhances positioning accuracy by utilizing inter-UAV relative ranging and cooperative localization, with a focus on correcting yaw angle errors. Each UAV is equipped with an inertial measurement unit (IMU) and an ultra-wideband (UWB) ranging module. IMU data is denoised using both low-pass and Kalman filters. Yaw angles are estimated from variations in relative distances, leading to a nonlinear system solved using the least squares method and the Limited-memory Broyden-Fletcher-Goldfarb-Shanno with Bounds (L-BFGS-B) algorithm. Simulation results under constant-altitude flight conditions demonstrate that the method significantly improves positioning accuracy, offering a practical and low-cost solution for inertial navigation.

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Cooperative Positioning Strategy for Multiple Unmanned Aerial Vehicles Based on Yaw Angle Correction

  • 会议论文

  • 发表日期：

    2026年

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  • Jingqing Yang,
  • Xuancheng You,
  • Baoli Ma,
  • Lixia Yan

摘要

This paper presents a cooperative positioning method for multiple unmanned aerial vehicles (UAVs) based on yaw angle correction. As UAVs play an increasingly critical role in the expanding low-altitude airspace economy, achieving accurate and cost-effective localization remains a significant challenge. The proposed approach enhances positioning accuracy by utilizing inter-UAV relative ranging and cooperative localization, with a focus on correcting yaw angle errors. Each UAV is equipped with an inertial measurement unit (IMU) and an ultra-wideband (UWB) ranging module. IMU data is denoised using both low-pass and Kalman filters. Yaw angles are estimated from variations in relative distances, leading to a nonlinear system solved using the least squares method and the Limited-memory Broyden-Fletcher-Goldfarb-Shanno with Bounds (L-BFGS-B) algorithm. Simulation results under constant-altitude flight conditions demonstrate that the method significantly improves positioning accuracy, offering a practical and low-cost solution for inertial navigation.